Rock, paper, scissors convolutional model¶

our goal is to construct a model for the popular game of Rock, Paper, Scissors. This game predominantly revolves around the choices made by players, specifically between scissors, rock, or paper. This characteristic enables us to employ image classification techniques to effectively model the game.

our approach involves capturing the hand gestures of the player and predicting whether they represent rock, paper, or scissors. Subsequently, the computer will make a random selection, leading to a determination of the winner.

As part of our methodology, we utilized the TensorFlow Datasets library, which offers of datasets seamlessly compatible with TensorFlow. For the construction of our model, we made use of the rock paper scissors dataset readily available within the TensorFlow library.

It is worth to mention that we get the idea from the tensorflow dataset and the following notebook, but we built our own model to implement image classification.

https://colab.research.google.com/github/trekhleb/machine-learning-experiments/blob/master/experiments/rock_paper_scissors_cnn/rock_paper_scissors_cnn.ipynb#scrollTo=DJ8jGFnTLt8t

Un-comment and run if your environment is missing tensorflow datasets package.¶
In [1]:
#! pip install tensorflow_datasets
In [2]:
import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly
from PIL import Image

plotly.offline.init_notebook_mode()

/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']
caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']
  warnings.warn(f"unable to load libtensorflow_io_plugins.so: {e}")
/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']
caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']
  warnings.warn(f"file system plugins are not loaded: {e}")
In [3]:
# Load the Rock Paper Scissors dataset
(train_dataset,test_dataset)  , dataset_info = tfds.load('rock_paper_scissors', data_dir='tmp', with_info=True,as_supervised=True, split=[tfds.Split.TRAIN, tfds.Split.TEST])

Downloading and preparing dataset 219.53 MiB (download: 219.53 MiB, generated: Unknown size, total: 219.53 MiB) to tmp/rock_paper_scissors/3.0.0...

Dl Completed...: 0 url [00:00, ? url/s]
Dl Size...: 0 MiB [00:00, ? MiB/s]
Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]
Generating train examples...:   0%|          | 0/2520 [00:00<?, ? examples/s]
Shuffling tmp/rock_paper_scissors/3.0.0.incomplete1XC258/rock_paper_scissors-train.tfrecord*...:   0%|        …
Generating test examples...:   0%|          | 0/372 [00:00<?, ? examples/s]
Shuffling tmp/rock_paper_scissors/3.0.0.incomplete1XC258/rock_paper_scissors-test.tfrecord*...:   0%|         …
Dataset rock_paper_scissors downloaded and prepared to tmp/rock_paper_scissors/3.0.0. Subsequent calls will reuse this data.
In [4]:
dataset_info
Out[4]:
tfds.core.DatasetInfo(
    name='rock_paper_scissors',
    full_name='rock_paper_scissors/3.0.0',
    description="""
    Images of hands playing rock, paper, scissor game.
    """,
    homepage='http://laurencemoroney.com/rock-paper-scissors-dataset',
    data_path=PosixGPath('/tmp/tmp5upmxuhxtfds'),
    file_format=tfrecord,
    download_size=219.53 MiB,
    dataset_size=219.23 MiB,
    features=FeaturesDict({
        'image': Image(shape=(300, 300, 3), dtype=uint8),
        'label': ClassLabel(shape=(), dtype=int64, num_classes=3),
    }),
    supervised_keys=('image', 'label'),
    disable_shuffling=False,
    splits={
        'test': <SplitInfo num_examples=372, num_shards=1>,
        'train': <SplitInfo num_examples=2520, num_shards=2>,
    },
    citation="""@ONLINE {rps,
    author = "Laurence Moroney",
    title = "Rock, Paper, Scissors Dataset",
    month = "feb",
    year = "2019",
    url = "http://laurencemoroney.com/rock-paper-scissors-dataset"
    }""",
)
In [5]:
NUM_TRAIN_EXAMPLES = dataset_info.splits['train'].num_examples
NUM_TEST_EXAMPLES = dataset_info.splits['test'].num_examples
NUM_CLASSES = dataset_info.features['label'].num_classes

print('Number of TRAIN examples:', NUM_TRAIN_EXAMPLES)
print('Number of TEST examples:', NUM_TEST_EXAMPLES)
print('Number of label classes:', NUM_CLASSES)

Number of TRAIN examples: 2520
Number of TEST examples: 372
Number of label classes: 3
In [6]:
INPUT_IMG_SIZE_ORIGINAL = dataset_info.features['image'].shape[0]
INPUT_IMG_SHAPE_ORIGINAL = dataset_info.features['image'].shape

INPUT_IMG_SIZE_REDUCED = INPUT_IMG_SIZE_ORIGINAL // 2
INPUT_IMG_SHAPE_REDUCED = (
    INPUT_IMG_SIZE_REDUCED,
    INPUT_IMG_SIZE_REDUCED,
    INPUT_IMG_SHAPE_ORIGINAL[2]
)
INPUT_IMG_SIZE = INPUT_IMG_SIZE_REDUCED
INPUT_IMG_SHAPE = INPUT_IMG_SHAPE_REDUCED

print (INPUT_IMG_SHAPE)

(150, 150, 3)
In [7]:
get_label_name = dataset_info.features['label'].int2str
def preview_dataset(dataset):
    plt.figure(figsize=(12, 12))
    plot_index = 0
    for features in dataset.take(12):
        (image, label) = features
        plot_index += 1
        plt.subplot(3, 4, plot_index)
        # plt.axis('Off')
        label = get_label_name(label.numpy())
        plt.title('Label: %s' % label)
        plt.imshow(image.numpy())

Pre-Process the dataset¶

In [8]:
def preprocess_image(image , label):
    image = tf.cast(image, tf.float32) / 255.0
    image = tf.image.resize(image , [INPUT_IMG_SIZE, INPUT_IMG_SIZE])
    return image, label


train_dataset = train_dataset.map(preprocess_image)
test_dataset= test_dataset.map(preprocess_image)
In [9]:
(first_image, first_lable) = list(train_dataset)[0]
print('Label:', first_lable.numpy(), '\n')
print('Image shape:', first_image.numpy().shape, '\n')
print(first_image.numpy())

Label: 2 

Image shape: (150, 150, 3) 

[[[0.995098   0.995098   0.995098  ]
  [0.995098   0.995098   0.995098  ]
  [0.995098   0.995098   0.995098  ]
  ...
  [0.9852941  0.9852941  0.9852941 ]
  [0.9843137  0.9843137  0.9843137 ]
  [0.98039216 0.98039216 0.98039216]]

 [[0.99607843 0.99607843 0.99607843]
  [0.995098   0.995098   0.995098  ]
  [0.995098   0.995098   0.995098  ]
  ...
  [0.98333335 0.98333335 0.98333335]
  [0.9813726  0.9813726  0.9813726 ]
  [0.98333335 0.98333335 0.98333335]]

 [[0.99607843 0.99607843 0.99607843]
  [0.9941176  0.9941176  0.9941176 ]
  [0.9941176  0.9941176  0.9941176 ]
  ...
  [0.9852941  0.9852941  0.9852941 ]
  [0.9852941  0.9852941  0.9852941 ]
  [0.9813726  0.9813726  0.9813726 ]]

 ...

 [[0.9862745  0.9862745  0.9862745 ]
  [0.98725486 0.98725486 0.98725486]
  [0.9882353  0.9882353  0.9882353 ]
  ...
  [0.9705882  0.9705882  0.9705882 ]
  [0.97352946 0.97352946 0.97352946]
  [0.9754902  0.9754902  0.9754902 ]]

 [[0.9882353  0.9882353  0.9882353 ]
  [0.98725486 0.98725486 0.98725486]
  [0.9862745  0.9862745  0.9862745 ]
  ...
  [0.9676471  0.9676471  0.9676471 ]
  [0.97156864 0.97156864 0.97156864]
  [0.972549   0.972549   0.972549  ]]

 [[0.9911765  0.9911765  0.9911765 ]
  [0.9862745  0.9862745  0.9862745 ]
  [0.9882353  0.9882353  0.9882353 ]
  ...
  [0.97352946 0.97352946 0.97352946]
  [0.9705882  0.9705882  0.9705882 ]
  [0.97352946 0.97352946 0.97352946]]]

Data Exploration:¶

In [10]:
preview_dataset(train_dataset)

Data Augmentation¶

This include rotating images, flipping images, changing color, and zoom. Augmentations taken from: https://www.wouterbulten.nl/posts/data-augmentation-using-tensorflow-data-dataset/

In [11]:
def flip(x: tf.Tensor) -> tf.Tensor:
    """Flip augmentation

    Args:
        x: Image to flip

    Returns:
        Augmented image
    """
    x = tf.image.random_flip_left_right(x)
    x = tf.image.random_flip_up_down(x)

    return x



def color(x: tf.Tensor) -> tf.Tensor:
    """Color augmentation

    Args:
        x: Image

    Returns:
        Augmented image
    """
    x = tf.image.random_hue(x, 0.08)
    x = tf.image.random_saturation(x, 0.6, 1.6)
    x = tf.image.random_brightness(x, 0.09)
    x = tf.image.random_contrast(x, 0.1, 1.3)
    return x

def rotate(x: tf.Tensor) -> tf.Tensor:
    """Rotation augmentation

    Args:
        x: Image

    Returns:
        Augmented image
    """

    return tf.image.rot90(x, tf.random.uniform(shape=[], minval=0, maxval=4, dtype=tf.int32))


def zoom(x: tf.Tensor) -> tf.Tensor:
    """Zoom augmentation

    Args:
        x: Image

    Returns:
        Augmented image
    """

    # Generate 20 crop settings, ranging from a 1% to 20% crop.
    scales = list(np.arange(0.8, 1.0, 0.01))
    boxes = np.zeros((len(scales), 4))

    for i, scale in enumerate(scales):
        x1 = y1 = 0.5 - (0.5 * scale)
        x2 = y2 = 0.5 + (0.5 * scale)
        boxes[i] = [x1, y1, x2, y2]

    def random_crop(img):
        # Create different crops for an image
        crops = tf.image.crop_and_resize([img], boxes=boxes, box_indices=np.zeros(len(scales)), crop_size=(32, 32))
        # Return a random crop
        return crops[tf.random.uniform(shape=[], minval=0, maxval=len(scales), dtype=tf.int32)]


    choice = tf.random.uniform(shape=[], minval=0., maxval=1., dtype=tf.float32)

    # Only apply cropping 50% of the time
    return tf.cond(choice < 0.5, lambda: x, lambda: random_crop(x))

def augment_data(image, label):
    image = flip(image)
    image = color(image)
    image = rotate(image)
    #image = zoom(image)
    return image, label
In [12]:
train_dataset_augmented = train_dataset.map(augment_data)
preview_dataset(train_dataset_augmented)

Data Shuffling and Batching¶

in order to prevent the model learning from order or grouping of images, we will shuffle the training examples.

In [13]:
BATCH_SIZE = 32

train_dataset_augmented_shuffled = train_dataset_augmented.shuffle(
    buffer_size=1000
)

train_dataset_augmented_shuffled = train_dataset_augmented.batch(
    batch_size=BATCH_SIZE
)

# Prefetch will enable the input pipeline to asynchronously fetch batches while your model is training.
dataset_train_augmented_shuffled = train_dataset_augmented_shuffled.prefetch(
    buffer_size=tf.data.experimental.AUTOTUNE
)

test_dataset_shuffled = test_dataset.batch(BATCH_SIZE)
In [14]:
print(train_dataset_augmented_shuffled)
print(test_dataset_shuffled)

<_BatchDataset element_spec=(TensorSpec(shape=(None, 150, 150, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>
<_BatchDataset element_spec=(TensorSpec(shape=(None, 150, 150, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>
In [15]:
# Define the CNN model

#1
model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=INPUT_IMG_SHAPE),
    tf.keras.layers.MaxPooling2D((2, 2)),
#2
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
#3
    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
#4
    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),

    tf.keras.layers.Flatten(),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(3, activation='softmax')
])
In [16]:
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 148, 148, 64)      1792      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 74, 74, 64)       0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 72, 72, 64)        36928     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 36, 36, 64)       0         
 2D)                                                             
                                                                 
 conv2d_2 (Conv2D)           (None, 34, 34, 128)       73856     
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 17, 17, 128)      0         
 2D)                                                             
                                                                 
 conv2d_3 (Conv2D)           (None, 15, 15, 128)       147584    
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 7, 7, 128)        0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 6272)              0         
                                                                 
 dropout (Dropout)           (None, 6272)              0         
                                                                 
 dense (Dense)               (None, 512)               3211776   
                                                                 
 dense_1 (Dense)             (None, 3)                 1539      
                                                                 
=================================================================
Total params: 3,473,475
Trainable params: 3,473,475
Non-trainable params: 0
_________________________________________________________________
In [17]:
tf.keras.utils.plot_model(
    model,
    show_shapes=True,
    show_layer_names=True,
)
Out[17]:
In [18]:
# we can also add learning rate to optimizer
# adam_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
rmsprop_optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.001)

# Compile cnn model
model.compile(optimizer= rmsprop_optimizer , loss='sparse_categorical_crossentropy', metrics=['accuracy'])
In [19]:
# Train cnn mmodel
model.fit(train_dataset_augmented_shuffled, epochs=40)

Epoch 1/40
79/79 [==============================] - 15s 64ms/step - loss: 1.1370 - accuracy: 0.3790
Epoch 2/40
79/79 [==============================] - 4s 54ms/step - loss: 0.8969 - accuracy: 0.5909
Epoch 3/40
79/79 [==============================] - 6s 70ms/step - loss: 0.5589 - accuracy: 0.7881
Epoch 4/40
79/79 [==============================] - 4s 54ms/step - loss: 0.3498 - accuracy: 0.8956
Epoch 5/40
79/79 [==============================] - 4s 54ms/step - loss: 0.2546 - accuracy: 0.9242
Epoch 6/40
79/79 [==============================] - 4s 53ms/step - loss: 0.1601 - accuracy: 0.9536
Epoch 7/40
79/79 [==============================] - 4s 55ms/step - loss: 0.1425 - accuracy: 0.9663
Epoch 8/40
79/79 [==============================] - 5s 64ms/step - loss: 0.0912 - accuracy: 0.9746
Epoch 9/40
79/79 [==============================] - 4s 55ms/step - loss: 0.0803 - accuracy: 0.9778
Epoch 10/40
79/79 [==============================] - 4s 53ms/step - loss: 0.0987 - accuracy: 0.9730
Epoch 11/40
79/79 [==============================] - 4s 55ms/step - loss: 0.0528 - accuracy: 0.9877
Epoch 12/40
79/79 [==============================] - 4s 54ms/step - loss: 0.0550 - accuracy: 0.9841
Epoch 13/40
79/79 [==============================] - 4s 53ms/step - loss: 0.0483 - accuracy: 0.9845
Epoch 14/40
79/79 [==============================] - 4s 54ms/step - loss: 0.0452 - accuracy: 0.9913
Epoch 15/40
79/79 [==============================] - 5s 60ms/step - loss: 0.0495 - accuracy: 0.9881
Epoch 16/40
79/79 [==============================] - 4s 56ms/step - loss: 0.0386 - accuracy: 0.9893
Epoch 17/40
79/79 [==============================] - 4s 53ms/step - loss: 0.0573 - accuracy: 0.9905
Epoch 18/40
79/79 [==============================] - 4s 54ms/step - loss: 0.0402 - accuracy: 0.9901
Epoch 19/40
79/79 [==============================] - 4s 53ms/step - loss: 0.0235 - accuracy: 0.9964
Epoch 20/40
79/79 [==============================] - 4s 53ms/step - loss: 0.0513 - accuracy: 0.9901
Epoch 21/40
79/79 [==============================] - 4s 55ms/step - loss: 0.0610 - accuracy: 0.9893
Epoch 22/40
79/79 [==============================] - 5s 60ms/step - loss: 0.0198 - accuracy: 0.9937
Epoch 23/40
79/79 [==============================] - 4s 55ms/step - loss: 0.0614 - accuracy: 0.9921
Epoch 24/40
79/79 [==============================] - 4s 57ms/step - loss: 0.0365 - accuracy: 0.9901
Epoch 25/40
79/79 [==============================] - 4s 56ms/step - loss: 0.0304 - accuracy: 0.9913
Epoch 26/40
79/79 [==============================] - 4s 53ms/step - loss: 0.0205 - accuracy: 0.9933
Epoch 27/40
79/79 [==============================] - 4s 54ms/step - loss: 0.0243 - accuracy: 0.9940
Epoch 28/40
79/79 [==============================] - 4s 53ms/step - loss: 0.0094 - accuracy: 0.9984
Epoch 29/40
79/79 [==============================] - 5s 62ms/step - loss: 0.0427 - accuracy: 0.9925
Epoch 30/40
79/79 [==============================] - 4s 56ms/step - loss: 0.0130 - accuracy: 0.9937
Epoch 31/40
79/79 [==============================] - 4s 53ms/step - loss: 0.0114 - accuracy: 0.9968
Epoch 32/40
79/79 [==============================] - 4s 53ms/step - loss: 0.0239 - accuracy: 0.9952
Epoch 33/40
79/79 [==============================] - 4s 54ms/step - loss: 0.0160 - accuracy: 0.9956
Epoch 34/40
79/79 [==============================] - 4s 55ms/step - loss: 0.0212 - accuracy: 0.9984
Epoch 35/40
79/79 [==============================] - 4s 54ms/step - loss: 0.0093 - accuracy: 0.9976
Epoch 36/40
79/79 [==============================] - 5s 60ms/step - loss: 0.0200 - accuracy: 0.9944
Epoch 37/40
79/79 [==============================] - 5s 58ms/step - loss: 0.0278 - accuracy: 0.9960
Epoch 38/40
79/79 [==============================] - 4s 55ms/step - loss: 0.0217 - accuracy: 0.9948
Epoch 39/40
79/79 [==============================] - 4s 53ms/step - loss: 0.0618 - accuracy: 0.9952
Epoch 40/40
79/79 [==============================] - 4s 54ms/step - loss: 0.0118 - accuracy: 0.9980
Out[19]:
<keras.callbacks.History at 0x7a36e020fb80>
In [20]:
# Evaluate cnn model
model.evaluate(test_dataset_shuffled)

12/12 [==============================] - 1s 104ms/step - loss: 1.2679 - accuracy: 0.9462
Out[20]:
[1.2678923606872559, 0.9462365508079529]
In [21]:
BATCH_SIZE = 32
steps_per_epoch = NUM_TRAIN_EXAMPLES // BATCH_SIZE
validation_steps = NUM_TEST_EXAMPLES // BATCH_SIZE

print('steps_per_epoch:', steps_per_epoch)
print('validation_steps:', validation_steps)

steps_per_epoch: 78
validation_steps: 11
In [22]:
!rm -rf tmp/checkpoints
!rm -rf logs
In [23]:
import os
import datetime
# Preparing callbacks.
os.makedirs('logs/fit', exist_ok=True)
tensorboard_log_dir = 'logs/fit/' + datetime.datetime.now().strftime('%Y%m%d-%H%M%S')
tensorboard_callback = tf.keras.callbacks.TensorBoard(
    log_dir=tensorboard_log_dir,
    histogram_freq=1
)

os.makedirs('tmp/checkpoints', exist_ok=True)
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath='tmp/checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5'
)

early_stopping_callback = tf.keras.callbacks.EarlyStopping(
    patience=5,
    monitor='val_accuracy'
    # monitor='val_loss'
)
In [24]:
training_history = model.fit(
    x= train_dataset_augmented_shuffled.repeat(),
    validation_data=test_dataset_shuffled.repeat(),
    epochs=40,
    steps_per_epoch=steps_per_epoch,
    validation_steps=validation_steps,
    callbacks=[
        # model_checkpoint_callback,
        # early_stopping_callback,
        tensorboard_callback
    ],
    verbose=1
)

Epoch 1/40
78/78 [==============================] - 6s 73ms/step - loss: 0.0322 - accuracy: 0.9948 - val_loss: 0.6279 - val_accuracy: 0.9545
Epoch 2/40
78/78 [==============================] - 6s 81ms/step - loss: 0.0173 - accuracy: 0.9972 - val_loss: 0.9980 - val_accuracy: 0.9489
Epoch 3/40
78/78 [==============================] - 6s 75ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.0060 - val_accuracy: 0.9517
Epoch 4/40
78/78 [==============================] - 6s 74ms/step - loss: 0.0210 - accuracy: 0.9972 - val_loss: 1.1027 - val_accuracy: 0.9545
Epoch 5/40
78/78 [==============================] - 6s 75ms/step - loss: 0.0214 - accuracy: 0.9948 - val_loss: 1.1194 - val_accuracy: 0.9517
Epoch 6/40
78/78 [==============================] - 5s 65ms/step - loss: 0.0261 - accuracy: 0.9956 - val_loss: 0.9814 - val_accuracy: 0.9318
Epoch 7/40
78/78 [==============================] - 5s 67ms/step - loss: 0.0193 - accuracy: 0.9964 - val_loss: 2.0795 - val_accuracy: 0.8636
Epoch 8/40
78/78 [==============================] - 6s 78ms/step - loss: 0.0195 - accuracy: 0.9964 - val_loss: 0.6560 - val_accuracy: 0.9403
Epoch 9/40
78/78 [==============================] - 5s 64ms/step - loss: 0.0073 - accuracy: 0.9980 - val_loss: 0.8258 - val_accuracy: 0.8920
Epoch 10/40
78/78 [==============================] - 5s 66ms/step - loss: 0.0136 - accuracy: 0.9988 - val_loss: 0.9657 - val_accuracy: 0.9205
Epoch 11/40
78/78 [==============================] - 5s 66ms/step - loss: 0.0166 - accuracy: 0.9968 - val_loss: 0.9379 - val_accuracy: 0.9347
Epoch 12/40
78/78 [==============================] - 6s 82ms/step - loss: 0.0215 - accuracy: 0.9980 - val_loss: 0.9865 - val_accuracy: 0.9091
Epoch 13/40
78/78 [==============================] - 7s 88ms/step - loss: 0.0190 - accuracy: 0.9980 - val_loss: 0.6597 - val_accuracy: 0.9489
Epoch 14/40
78/78 [==============================] - 6s 72ms/step - loss: 0.0049 - accuracy: 0.9992 - val_loss: 1.5706 - val_accuracy: 0.9205
Epoch 15/40
78/78 [==============================] - 5s 64ms/step - loss: 0.0302 - accuracy: 0.9956 - val_loss: 0.5279 - val_accuracy: 0.9659
Epoch 16/40
78/78 [==============================] - 5s 70ms/step - loss: 0.0061 - accuracy: 0.9984 - val_loss: 0.8885 - val_accuracy: 0.8750
Epoch 17/40
78/78 [==============================] - 5s 63ms/step - loss: 0.0132 - accuracy: 0.9976 - val_loss: 0.5495 - val_accuracy: 0.9290
Epoch 18/40
78/78 [==============================] - 5s 69ms/step - loss: 0.0044 - accuracy: 0.9996 - val_loss: 1.3008 - val_accuracy: 0.8750
Epoch 19/40
78/78 [==============================] - 6s 79ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 3.3813 - val_accuracy: 0.8182
Epoch 20/40
78/78 [==============================] - 5s 66ms/step - loss: 0.0167 - accuracy: 0.9976 - val_loss: 1.0709 - val_accuracy: 0.9091
Epoch 21/40
78/78 [==============================] - 5s 63ms/step - loss: 0.0062 - accuracy: 0.9980 - val_loss: 1.1053 - val_accuracy: 0.9489
Epoch 22/40
78/78 [==============================] - 5s 68ms/step - loss: 0.0311 - accuracy: 0.9972 - val_loss: 0.8387 - val_accuracy: 0.9290
Epoch 23/40
78/78 [==============================] - 5s 67ms/step - loss: 0.0049 - accuracy: 0.9992 - val_loss: 0.6118 - val_accuracy: 0.9347
Epoch 24/40
78/78 [==============================] - 5s 67ms/step - loss: 0.0098 - accuracy: 0.9976 - val_loss: 0.6217 - val_accuracy: 0.9432
Epoch 25/40
78/78 [==============================] - 6s 77ms/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 0.8800 - val_accuracy: 0.9460
Epoch 26/40
78/78 [==============================] - 5s 60ms/step - loss: 0.0149 - accuracy: 0.9976 - val_loss: 1.4190 - val_accuracy: 0.8949
Epoch 27/40
78/78 [==============================] - 5s 66ms/step - loss: 0.0173 - accuracy: 0.9976 - val_loss: 2.3274 - val_accuracy: 0.8636
Epoch 28/40
78/78 [==============================] - 6s 72ms/step - loss: 0.0070 - accuracy: 0.9992 - val_loss: 0.8087 - val_accuracy: 0.9290
Epoch 29/40
78/78 [==============================] - 5s 67ms/step - loss: 0.0118 - accuracy: 0.9984 - val_loss: 1.3302 - val_accuracy: 0.8864
Epoch 30/40
78/78 [==============================] - 5s 66ms/step - loss: 0.0086 - accuracy: 0.9984 - val_loss: 0.9249 - val_accuracy: 0.9460
Epoch 31/40
78/78 [==============================] - 6s 80ms/step - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.8826 - val_accuracy: 0.9347
Epoch 32/40
78/78 [==============================] - 5s 61ms/step - loss: 0.0098 - accuracy: 0.9976 - val_loss: 0.7692 - val_accuracy: 0.9574
Epoch 33/40
78/78 [==============================] - 5s 68ms/step - loss: 1.1891e-04 - accuracy: 1.0000 - val_loss: 0.8732 - val_accuracy: 0.9574
Epoch 34/40
78/78 [==============================] - 6s 71ms/step - loss: 0.0111 - accuracy: 0.9984 - val_loss: 0.6035 - val_accuracy: 0.9517
Epoch 35/40
78/78 [==============================] - 5s 61ms/step - loss: 0.0048 - accuracy: 0.9996 - val_loss: 0.6229 - val_accuracy: 0.9403
Epoch 36/40
78/78 [==============================] - 5s 69ms/step - loss: 0.0089 - accuracy: 0.9964 - val_loss: 0.9306 - val_accuracy: 0.9205
Epoch 37/40
78/78 [==============================] - 6s 81ms/step - loss: 6.4737e-05 - accuracy: 1.0000 - val_loss: 1.4043 - val_accuracy: 0.9034
Epoch 38/40
78/78 [==============================] - 5s 68ms/step - loss: 0.0064 - accuracy: 0.9988 - val_loss: 0.7957 - val_accuracy: 0.9318
Epoch 39/40
78/78 [==============================] - 5s 68ms/step - loss: 0.0042 - accuracy: 0.9996 - val_loss: 0.8129 - val_accuracy: 0.9290
Epoch 40/40
78/78 [==============================] - 5s 64ms/step - loss: 1.7076e-04 - accuracy: 1.0000 - val_loss: 0.7205 - val_accuracy: 0.9460
In [25]:
def render_training_history(training_history):
    loss = training_history.history['loss']
    val_loss = training_history.history['val_loss']

    accuracy = training_history.history['accuracy']
    val_accuracy = training_history.history['val_accuracy']

    plt.figure(figsize=(14, 4))

    plt.subplot(1, 2, 1)
    plt.title('Loss')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.plot(loss, label='Training set')
    plt.plot(val_loss, label='Test set', linestyle='--')
    plt.legend()
    plt.grid(linestyle='--', linewidth=1, alpha=0.5)

    plt.subplot(1, 2, 2)
    plt.title('Accuracy')
    plt.xlabel('Epoch')
    plt.ylabel('Accuracy')
    plt.plot(accuracy, label='Training set')
    plt.plot(val_accuracy, label='Test set', linestyle='--')
    plt.legend()
    plt.grid(linestyle='--', linewidth=1, alpha=0.5)

    plt.show()
In [26]:
render_training_history(training_history)
In [27]:
import plotly.express as px
import matplotlib.pyplot as plt


# Assuming you have the accuracy values stored in lists or arrays
accuracy = training_history.history['accuracy']
val_accuracy = training_history.history['val_accuracy']

# Assuming epoch values range from 0 to 39 (40 values)
epoch = list(range(40))

# Create the scatter plot
fig = px.scatter()

# Add the training accuracy trace
fig.add_scatter(x=epoch, y=accuracy, name='Training set')

# Add the validation accuracy trace
fig.add_scatter(x=epoch, y=val_accuracy, name='Validation set')

fig.update_layout(
    title='Accuracy vs Epoch',
    xaxis_title='Epoch',
    yaxis_title='Accuracy',
    yaxis_range=[0, 1.0]  # Set the range of the y-axis for accuracy
)

fig.show()
In [28]:
import plotly.express as px

# Assuming you have the loss values stored in lists or arrays
loss = training_history.history['loss']
val_loss = training_history.history['val_loss']

# Assuming epoch values range from 0 to 39 (40 values)
epoch = list(range(40))

# Create the scatter plot
fig = px.scatter()

# Add the training loss trace
fig.add_scatter(x=epoch, y=loss, name='Training Loss')

# Add the validation loss trace
fig.add_scatter(x=epoch, y=val_loss, name='Validation Loss')

fig.update_layout(
    title='Loss vs Epoch',
    xaxis_title='Epoch',
    yaxis_title='Loss',
    yaxis=dict(range=[0, 4])  # Set the range of the y-axis for loss
)

fig.show()
In [29]:
# save cnn model
model_name = 'rock_paper_scissors_cnn_updated.h5'
model.save(model_name, save_format='h5')
In [30]:
! pip install plotly

Requirement already satisfied: plotly in /opt/conda/lib/python3.10/site-packages (5.14.1)
Requirement already satisfied: tenacity>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from plotly) (8.2.2)
Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from plotly) (21.3)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->plotly) (3.0.9)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
In [ ]:
import matplotlib.pyplot as plt
plt.switch_backend('tkagg')

Formula using Latex¶

$$ \text{{accuracy}} = \frac{{\text{{correct predictions}}}}{{\text{{total predictions}}}} $$